Minimal Intervention Shared Control with Guaranteed Safety under Non-Convex Constraints
Shivam Chaubey, Francesco Verdoja, Shankar Deka, Ville Kyrki
AI summary
Problem
Existing shared control methods struggle to guarantee strict safety and recursive feasibility under non-convex constraints while preserving user intent and scaling to real-time applications.
Approach
The authors propose a Constraint-Aware Assistive Controller that leverages precomputed Robust Controlled Invariant Sets for safety and reformulates non-convex constraints into a sparse Mixed-Integer Quadratic Program for real-time, minimal-intervention control.
Key results
- Guaranteed recursive feasibility and safety under non-convex constraints via RCIS and MIQP
- Large-scale user study (66 participants) demonstrating reduced workload, increased trust, and preserved control authority
- Real-world robotic manipulator validation confirming collision-free operation under bounded disturbances
- Real-time computational efficiency achieved through single-step optimization and sparse constraint encoding
Why it matters
Provides a practical, mathematically rigorous foundation for safe and intuitive human-robot collaboration in teleoperation, assistive robotics, and semi-autonomous systems.
Abstract
Shared control combines human intention with autonomous decision-making. At the low level, the primary goal is to maintain safety regardless of the user’s input to the system. However, existing shared control methods—based on, e.g., Model Predictive Control, Control Barrier Functions, or learning-based control—often face challenges with feasibility, scalability, and mixed constraints. To address these challenges, we propose a Constraint-Aware Assistive Controller that computes control actions online while ensuring recursive feasibility, strict constraint satisfaction, and minimal deviation from the user’s intent. It also accommodates a structured class of non-convex constraints common in real- world settings. We leverage Robust Controlled Invariant Sets for recursive feasibility and a Mixed-Integer Quadratic Pro- gramming formulation to handle non-convex constraints. We validate the approach through a large-scale user study with 66 participants—one of the most extensive in shared control research—using a simulated environment to assess task load, trust, and perceived control, in addition to performance. The results show consistent improvements across all these aspects without compromising safety and user intent. Additionally, a real-world experiment on a robotic manipulator demonstrates the framework’s applicability under bounded disturbances, ensuring safety and collision-free operation.